Normal Approximation of Binomial (Edexcel A-Level Mathematics): Revision Notes
4.4.2 Normal Approximation of Binomial Distribution
The Normal approximation to the Binomial distribution is a useful technique when dealing with a large number of trials in a Binomial experiment. This approximation allows us to use the Normal distribution to estimate Binomial probabilities, which can be easier and more practical, especially when dealing with large sample sizes.
When to Use Normal Approximation
You can use the Normal approximation to the Binomial distribution when the following conditions are met:
- Large Number of Trials (n): The sample size should be large.
- Probability Conditions:
- These conditions ensure that the Binomial distribution is sufficiently "bell-shaped" to be approximated by a Normal distribution.
Example: Tossing a Biased Coin Suppose you have a biased coin that lands on heads % of the time. You flip the coin times. What is the probability of getting between and heads (inclusive)?
Step 1: Define the Binomial Distribution
- Number of trials (n):
- Probability of success (p):
- Random variable X: Number of heads in .
Step 2: Check Conditions for Normal Approximation
- Both and are greater than , so the Normal approximation is appropriate.
Step 3: Determine the Mean (μ) and Standard Deviation (σ)
- Mean (μ):
- Standard deviation (σ):
Step 4: Apply the Continuity Correction Since the Normal distribution is continuous and the Binomial is discrete, apply a continuity correction when converting Binomial probabilities to Normal probabilities. This involves adjusting the bounds by .
- To find in the Binomial distribution, approximate this by in the Normal distribution.
Step 5: Convert to Z-scores Convert the adjusted values to :
Step 6: Use the Z-table Look up the in the (or use a calculator):
Step 7: Calculate the Required Probability Subtract the smaller probability from the larger one to find the probability of getting between and :
So, the probability of getting between and is approximately .
Conclusion
The Normal approximation to the Binomial distribution is a powerful tool for estimating probabilities when dealing with a large number of trials. By converting the Binomial problem into a Normal distribution problem, applying a continuity correction, and using , you can efficiently find probabilities that would otherwise be cumbersome to calculate using the Binomial distribution directly. This technique is particularly useful in exams as it can simplify complex calculations.